num_trials=num_alpha,
prefix=te_prefix,
inp=inp,
noise_amp=noise_test,
run_time=run_time,
N_AL=N_AL,
train=False)
#==============================================================================================================
# Create Base Odors' Currents
#==============================================================================================================
I_arr = []
# Create all (num_odors_train) of the base odors
for i in range(num_odors_train):
I = ex.get_rand_I(N_AL, p=0.33, I_inp=inp) * nA
I_arr.append(I)
#==============================================================================================================
# Choose which base currents to mix
#==============================================================================================================
listOfNumbers = np.arange(
0, 15, 1,
dtype='int') # Generate numbers in order (guarantees no duplicates)
np.random.shuffle(listOfNumbers) # Randomize order of numbers
baseNum = listOfNumbers.reshape(
(5, 3)) # reshape randomized list of numbers into usable array
print(baseNum)
np.savetxt('baseNumbersUsed.txt', baseNum)
#==============================================================================================================
# Run Base Odor and Mixture Odor Simulations
@network_operation(dt=5 * ms)
def f2(t):
print(G_AL.I_inj[0])
net.add(f2)
# random input
num_classes = 2
samples_per_class = 1
n_samples = int(samples_per_class * num_classes)
p_inj = 0.3
X = np.zeros((num_classes, N_AL))
for j in range(num_classes):
X[j, :] = ex.get_rand_I(N_AL, p_inj, 1)
# troubleshooting array
#test_array = np.zeros(N_AL)
#test_array[0] = 0.5
#test_array[999] = 1.0
# set tstart!
tstart = reset_time * ms
# Run random input with gradual current
for i in range(n_samples):
# turns off all neurons
G_AL.active_ = 0
net.run(reset_time * ms)
I_arr = []
# Use previous injected training currents if they exist
inj_curr = os.path.exists(tr_prefix + 'I_0.npy')
if inj_curr:
print('Using previous odors...')
for i in range(num_odors):
I_arr.append(np.load(tr_prefix + 'I_' + str(i) + '.npy'))
else:
#create the base odors
for i in range(num_odors):
#I = ex.get_rand_I(N_AL, p = np.random.uniform(0.1, 0.5), I_inp = inp)*nA
# Editing for Henry
I = ex.get_rand_I(N_AL, p = p_inj, I_inp = inp)*nA
I_arr.append(I)
# Since we doin't rescale currents when saving, this adjustment is necessary
I_arr = np.asarray(I_arr)/1e-9*nA
# example of running odors 5-10
#I_arr = I_arr[num_odors:]
run_params_train = dict(num_odors = num_odors,
num_trials = num_train,
prefix = tr_prefix,
inp = inp,
noise_amp = noise_amp,
run_time = run_time,
N_AL = N_AL,
neuron_class=nm.n_HH,
syn_class=nm.s_GABA_inh,
PAL=PAL,
mon=['V'])
#create the network object
net = Network()
G_AL, S_AL, trace_AL, spikes_AL = lm.get_AL(al_para, net)
net.store()
for k in range(3):
net.restore()
I = (ex.get_rand_I(N_AL, p=0.33, I_inp=15) + 15) * uA / cm**2
G_AL.I_inj = I
net.run(run_time, report='text')
np.save(prefix + 'spikes_t_' + str(k), spikes_AL.t / ms)
np.save(prefix + 'spikes_i_' + str(k), spikes_AL.i)
np.save(prefix + 'I_' + str(k), I)
np.save(prefix + 'trace_V_' + str(k), trace_AL.V)
np.save(prefix + 'trace_t_' + str(k), trace_AL.t)
spikes_t_arr, spikes_i_arr, I_arr, trace_V_arr, trace_t_arr = anal.load_wlc_data(
prefix, num_runs=3)
fig1 = plt.figure()
plt.plot(spikes_t_arr[0] / ms, spikes_i_arr[0], '.')
inp = 1.0
noise_amp = 0.1*np.sqrt(3) #max noise as a fraction of inp
noise_test = 0.1*np.sqrt(3)
# Different spatial injections
sp_odors = 1
num_odors = int(sp_odors*n_waveforms)
num_train = 1
num_test = 1
run_time = 100*ms
I_arr = []
#create the base odors
for i in range(sp_odors):
I = ex.get_rand_I(N_AL, p =1./3., I_inp = inp)
I_arr.append(I)
run_params_train = dict(num_odors = num_odors,
n_waveforms = n_waveforms,
sp_odors = sp_odors,
num_trials = num_train,
prefix = tr_prefix,
inp = inp*nA,
noise_amp = noise_amp,
run_time = run_time,
N_AL = N_AL,
train = True)